Leveraging Historical Data for High-Dimensional Regression Adjustment, a Composite Covariate Approach
The amount of data collected from patients involved in clinical trials is continuously growing. All patient characteristics are potential covariates that could be used to improve clinical trial analysis and power. However, the restricted number of patients in phases I and II studies limits the possible number of covariates included in the analyses. In this paper, we investigate the cost/benefit ratio of including covariates in the analysis of clinical trials. Within this context, we address the long-running question "What is the optimum number of covariates to include in a clinical trial?" To further improve the cost/benefit ratio of covariates, historical data can be leveraged to pre-specify the covariate weights, which can be viewed as the definition of a new composite covariate. We analyze the use of a composite covariate while estimating the treatment effect in small clinical trials. A composite covariate limits the loss of degrees of freedom and the risk of overfitting.
Code (0)
등록된 구현이 없습니다.
Tasks
regressionSimilar Papers 제목 키워드 기반
Learning Functional Priors and Posteriors from Data and Physics
We develop a new Bayesian framework based on deep neural networks to be able to extrapolate in space-time using historical data and to quantify uncertainties arising from both noisy and gappy data in physical problems. S…
Meta-LearningregressionUncertainty QuantificationAn Efficient Approach to Regression Problems with Tensor Neural Networks
This paper introduces a tensor neural network (TNN) to address nonparametric regression problems, leveraging its distinct sub-network structure to effectively facilitate variable separation and enhance the approximation …
Numerical IntegrationregressionHigh-dimensional mixed-frequency IV regression
This paper introduces a high-dimensional linear IV regression for the data sampled at mixed frequencies. We show that the high-dimensional slope parameter of a high-frequency covariate can be identified and accurately es…
regressionTime SeriesTime Series AnalysisVocal Bursts Intensity PredictionPrediction of adverse events in Afghanistan: regression analysis of time series data grouped not by geographic dependencies
The aim of this study was to approach a difficult regression task on highly unbalanced data regarding active theater of war in Afghanistan. Our focus was set on predicting the negative events number without distinguishin…
regressionTime SeriesTime Series AnalysisOn LASSO for High Dimensional Predictive Regression
This paper examines LASSO, a widely-used $L_{1}$-penalized regression method, in high dimensional linear predictive regressions, particularly when the number of potential predictors exceeds the sample size and numerous u…
regressionTime SeriesTime Series AnalysisVocal Bursts Intensity Prediction